Smart garments that identify user changes

ABSTRACT

Sensor data generated by a portion of a plurality of sensors integrated into a smart garment is received, the sensor data indicating at least one biometric parameter indicating a state of health of a user wearing the smart garment. Based on the first sensor data, at least one health change parameter indicating at least one change in the state of health of the user can be determined. Based on the at least one health change parameter, whether the at least one change in the state of health of the user exceeds a threshold value can be determined. Responsive to determining that the at least one change in the state of health of the user exceeds the threshold value, a notification indicating that the at least one change in the state of health of the user exceeds the threshold value can be output.

BACKGROUND

The present invention relates to smart fabrics and, more particularly,the use of smart fabrics in smart garments.

Smart fabrics are fabrics that include electronic components. Smartfabrics can perform tasks that traditional fabrics do not. For example,from an aesthetic perspective, smart fabrics can be illuminated and/orchange color. Smart fabrics also have been developed for protectiveclothing to guard against extreme environmental hazards like radiationand the effects of space travel. The health and beauty industry also istaking advantage of innovations such as drug-releasing medical fabrics,and fabrics that include moisturizer, perfume, and anti-agingproperties.

SUMMARY

A method includes receiving first sensor data generated by at least afirst portion of a plurality of sensors integrated into a smart garment,the first sensor data indicating at least one biometric parameterindicating a state of health of a user wearing the smart garment. Themethod also can include determining, using a processor, based on thefirst sensor data, at least one health change parameter indicating atleast one change in the state of health of the user. The method also caninclude, based on the at least one health change parameter indicatingthe at least one change in the state of health of the user, determiningwhether the at least one change in the state of health of the userexceeds a threshold value. The method also can include, responsive todetermining that the at least one change in the state of health of theuser exceeds the threshold value, outputting a first notificationindicating that the at least one change in the state of health of theuser exceeds the threshold value.

A system includes a processor programmed to initiate executableoperations. The executable operations receiving first sensor datagenerated by at least a first portion of a plurality of sensorsintegrated into a smart garment, the first sensor data indicating atleast one biometric parameter indicating a state of health of a userwearing the smart garment. The executable operations also can includedetermining based on the first sensor data, at least one health changeparameter indicating at least one change in the state of health of theuser. The executable operations also can include, based on the at leastone health change parameter indicating the at least one change in thestate of health of the user, determining whether the at least one changein the state of health of the user exceeds a threshold value. Theexecutable operations also can include, responsive to determining thatthe at least one change in the state of health of the user exceeds thethreshold value, outputting a first notification indicating that the atleast one change in the state of health of the user exceeds thethreshold value.

A computer program includes a computer readable storage medium havingprogram code stored thereon. The program code is executable by aprocessor to perform a method. The method includes receiving firstsensor data generated by at least a first portion of a plurality ofsensors integrated into a smart garment, the first sensor dataindicating at least one biometric parameter indicating a state of healthof a user wearing the smart garment. The method also can includedetermining, by the processor, based on the first sensor data, at leastone health change parameter indicating at least one change in the stateof health of the user. The method also can include, based on the atleast one health change parameter indicating the at least one change inthe state of health of the user, determining whether the at least onechange in the state of health of the user exceeds a threshold value. Themethod also can include, responsive to determining that the at least onechange in the state of health of the user exceeds the threshold value,outputting a first notification indicating that the at least one changein the state of health of the user exceeds the threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial diagram illustrating an example of a smart fabric.

FIG. 2 is a pictorial diagram illustrating an example of a smartgarment.

FIG. 3 is a block diagram illustrating an example of a data processingenvironment.

FIG. 4 is a block diagram illustrating example architecture for aserver.

FIG. 5 is a flow chart illustrating an example of a method of outputtinga notification regarding a size of a smart garment.

FIG. 6 is a flow chart illustrating an example of a method of outputtinga notification regarding a risk of disease for a user.

FIG. 7 is a flow chart illustrating an example of a method of outputtinga notification regarding a change in the state of health of a user.

DETAILED DESCRIPTION

This disclosure relates to smart fabrics and, more particularly, the useof smart fabrics in smart garments. In accordance with the inventivearrangements disclosed herein, a smart garment can include a pluralityof sensors that generate sensor data. The sensor data can includetension data representing tension, and thus stretch, in the smartgarment. The sensor data can be analyzed and, based on the analyses, anyof a variety of determinations can be made about a user who is wearingthe smart garment. For example, a determination can be made as towhether a size of the smart garment is suitable for the user. If not, anotification can be output to indicate to the user that the size is notsuitable for the user. Further, the notification can indicate anappropriate size that would be suitable for the user and/or a particulargarment having a size that is suitable for the user.

The sensor data further can include biometric parameters representing astate of health of the user. Based on the sensor data, determinationsregarding the user's health can be made. Based on such determinations,any of a variety of notifications can be communicated to the user and/ora care giver of the user. By way of example, the notifications canprovide recommendations to seek medical attention, to rest, to exercise,and so on. In a further example, based on the sensor data, adetermination of whether the user has a risk, greater than a thresholdvalue, of disease. The notifications can indicate such risk, and providerecommendations to mitigate the risk.

Several definitions that apply throughout this document now will bepresented.

As defined herein, the term “smart garment” means a garment made, atleast in part, of smart fabric.

As defined herein, the term “smart fabric” means a fabric that includesat least at least one electronic component.

As defined herein, the term “size” means a physical dimension.

As defined herein, the term “suitable” means appropriate for an intendedpurpose.

As defined herein, the term “client device” means a processing systemincluding at least one processor and memory that requests sharedservices from a server, and with which a user directly interacts.Examples of a client device include, but are not limited to, aworkstation, a desktop computer, a mobile computer, a laptop computer, anetbook computer, a tablet computer, a smart phone, a personal digitalassistant, a smart watch, smart glasses, a gaming device, a set-top box,a smart television and the like. Network infrastructure, such asrouters, firewalls, switches, access points and the like, are not clientdevices as the term “client device” is defined herein.

As defined herein, the term “server” means a processing system includingat least one processor and memory that shares services one or more othersystems and/or client devices.

As defined herein, the term “sensor” means a device that detects ormeasures a physical property and outputs corresponding data.

As defined herein, the term “processor” means at least one hardwarecircuit (e.g., an integrated circuit) configured to carry outinstructions contained in program code. Examples of a processor include,but are not limited to, a central processing unit (CPU), an arrayprocessor, a vector processor, a digital signal processor (DSP), afield-programmable gate array (FPGA), a programmable logic array (PLA),an application specific integrated circuit (ASIC), programmable logiccircuitry, and a controller.

As defined herein, the term “responsive to” means responding or reactingreadily to an action or event. Thus, if a second action is performed“responsive to” a first action, there is a causal relationship betweenan occurrence of the first action and an occurrence of the secondaction, and the term “responsive to” indicates such causal relationship.

As defined herein, the term “computer readable storage medium” means astorage medium that contains or stores program code for use by or inconnection with an instruction execution system, apparatus, or device.As defined herein, a “computer readable storage medium” is not atransitory, propagating signal per se.

As defined herein, the term “output” means storing in memory elements,writing to display or other peripheral output device, sending ortransmitting to another system, exporting, or similar operations.

As defined herein, the term “automatically” means without userintervention.

As defined herein, the term “user” means a person (i.e., a human being).

FIG. 1 is a pictorial diagram illustrating an example of a smart fabric100. The smart fabric 100 can include a processor 105. The smart fabric100 also can include an RF transmitter (hereinafter “transmitter”) 110configured to transmit RF signals. In one arrangement, the transmitter110 can be a component of a transceiver that also includes an RFreceiver, although the present arrangements are not limited in thisregard. The smart fabric 100 also can include a plurality of sensors 115integrated into the smart fabric 100.

The processor 105 can include a computer readable storage medium, forexample an erasable programmable read-only memory (EPROM or Flashmemory), in which computer program code is stored. The computer programcode can be executed by the processor, as will be described. Theprocessor 105 also can include an accelerometer that detects movement,and/or any other suitable sensors or measurement components. Further,the processor 105 can include a plurality of input/output (I/O) ports toconnect the processor to other devices, such as the transmitter 110 andthe plurality of sensors 115. In one arrangement, the transmitter 110can be a component of the processor 105.

Each of the plurality of sensors 115 can be communicatively linked tothe processor 105, and the processor 105 can be communicatively linkedto the transmitter 110. Electrical conductors (not shown) can beintegrated into the smart fabric 100 to provide communication linksbetween the processor 105 and the transmitter 110 and sensors 115. Thesmart fabric 100 also can include an energy source 120 that providespower to the processor 105, transmitter 110 and, optionally, the sensors115. The energy source 120 can include, for example, a battery, a solarcell, a piezo electric charger, an inductive power supply, and/or anyother devices that generate and/or provide electricity. In the case thatthe energy source 120 is an inductive power supply, the inductive powersupply can generate electricity in response to a magnetic fieldgenerated by an inductive charger, as is known to those of ordinaryskill in the art. Power can be conveyed from the energy source 120 tothe processor 105, transmitter 110 and, optionally, the sensors 115 viaelectrical conductors.

In one arrangement, the electrical conductors can be embedded in threadsof the smart fabric 100, for example by spinning the electricalconductors into the threads. In another arrangement, the electricalconductors can be woven with the threads into the smart fabric 100.Further, the sensors 115 can be embedded into the threads of the smartfabric 100 when the threads are spun or can be embedded into the smartfabric 100 when the smart fabric 100 is woven from the threads. Theprocessor 105 and transmitter 110 also can be embedded into the smartfabric 100 when the smart fabric 100 is woven from the threads, or canbe attached to the smart fabric 100 after the smart fabric 100 is woven.

In another arrangement, the processor 105, transmitter 110 and sensors115 can be embedded in a flexible material that is configured to beattached to fabric to form the smart fabric 100. For example, theflexible material can include a substrate into which the processor 105,transmitter 110, sensors 115 and conductors are embedded. The flexiblematerial can include an adhesive on at least one side configured toattach the flexible material to the fabric. In illustration, theadhesive can be configured to be activated with heat and/or light tobond the flexible material to the fabric. In an arrangement in which theadhesive is heat activated, the processor 105, transmitter 110, sensors115 and conductors can be configured to withstand the amount ofactivation heat without becoming damaged during the process of attachingthe flexible material to the fabric.

The transmitter 110 can be configured to receive signals from theprocessor 105, encode the signals, modulate the signals, etc. togenerate corresponding RF signals. For example, the transmitter 110 cangenerate RF signals in accordance with a suitable RF communicationprotocol, for example in accordance with one more IEEE 802-15 standards(e.g., Bluetooth®, Bluetooth® low energy (BLE), Zigbee®, and so on)and/or near field communication (NFC).

In one aspect, at least a portion of the sensors 115 can be tensionsensors (e.g., piezoelectric tension sensors, which are known in theart) configured to output to the processor 105 respective signalscorresponding to an amount of tension, and thus stretch, of the smartfabric 100. In a further arrangement, at least a portion of the sensors115 can be thermal sensors configured to output to the processor 105respective signals corresponding to a temperature of a user wearing thesmart fabric 100. Also, at least a portion of the sensors 115 can beperspiration sensors (or moisture sensors) configured to output to theprocessor 105 respective signals corresponding to a level ofperspiration (or moisture) of a user wearing the smart fabric 100.Further, at least a portion of the sensors 115 can be respirationsensors configured to output to the processor 105 respective signalscorresponding to a level of respiration of a user wearing the smartfabric 100. In a further arrangement, at least a portion of the sensors115 can be heart rate sensors configured to detect a heart rate of auser wearing the smart fabric 100.

Also, at least a portion of the sensors 115 can be blood pressuresensors configured to output to the processor 105 respective signalscorresponding to a level of blood pressure of a user wearing the smartfabric 100. In illustration, a garment into which the smart fabric 100is incorporated can include a blood pressure cuff. The processor 105 canactuate the blood pressure cuff to expand with a compressed gas (e.g.,air), and then slowly decompress. A portion of the sensors 115 can beconfigured to detect a systolic value and a diastolic value of theuser's pulse.

In addition, at least a portion of the sensors 115 can be can becapacitive sensors configured to output to the processor 105 respectivesignals indicating whether the smart fabric 100 is being worn. Forexample, such sensors can be configured to detect and indicate aproximity of the sensors 115 to biological tissue. For example, when agarment made of the smart fabric 100 is worn, one or more sensors 115may be placed proximate to a user's skin (e.g., within 0.5 mm, 1 mm, 2mm, 3 mm, 4 mm, 5 mm, etc.), and the signals can indicate such.

Still, other types of sensors 115 can be utilized, and the presentarrangements are not limited in this regard. In one arrangement, morethan one type of sensor 115 can be used. For example, the plurality ofsensors 115 can include one or more of the previously described sensors115 and/or one or more other types of sensors.

In one non-limiting arrangement, each sensor 115 also can include aradio frequency identifier (RFID) tag. Each RFID tag can include acomputer readable storage medium, for example an erasable programmableread-only memory (EPROM or Flash memory), configured to store respectivedata for the sensor 115. The data can include a unique identifier forthe respective sensor 115. In addition, each RFID tag also can include areceiver (or transceiver), a decoder, a power supply and a processorconfigured to detect an RF signal, decode the RF signal to identify datacontained in the RF signal, and also store the data contained in the RFsignal in the computer readable storage medium. The power supply cangenerate energy for the decoder and processor to operate from energycontained in the RF signal, as is known in the art. As will bedescribed, the data contained in the RF signal can indicate in whichcomponent of a smart garment the sensor 115 is integrated.

FIG. 2 is a pictorial diagram illustrating an example of a smart garment200. The smart garment 200 can include the smart fabric 100 of FIG. 1.In illustration, the smart garment 200 can be made of the smart fabric100. The smart garment 200 can be a shirt, a sweater, a jacket, pants, askirt, a dress, a hospital gown, a shoe, or any other type of garment.

In one arrangement, different components 205, 210, 215, 220 of the smartgarment 200 can be cut from the smart fabric 100, and perhaps one ormore other smart fabrics (not shown) following a garment pattern, andthe components 205-220 can be sewn together to create the smart garment200. The processor 105, transmitter 110 and energy source 120 can beintegrated into a respective portion of the smart fabric 100 used forany of the smart garment components 205-220, and the presentarrangements are not limited in this regard.

During the cutting process, various electrical conductors may be cut.During the sewing process, electrical connections to the sensors 115 canbe re-established by connecting ends respective ends of electricalconductors. For example, at a seam 230 where a sleeve 210 is connectedto a front 215 and back 220 of the smart garment 200, there may beelectrical conductors in the sleeve 210, front 215 and back 220 thathave been cut, and the electrical conductors of the sleeve 210 can beattached to the electrical conductors of the front 215 and back 220 ofthe smart garment 200 to form continuous electrical connections betweenthe processor 105 and the sensors 115. Since the sleeve 210, front 215and back 220 may be cut from different portions of the smart fabric 100,the electrical path between the processor 105 and each sensor 115 in thesleeve 210 need not be the same electrical path that was between theprocessor 105 and each of such sensors 115 in the smart fabric 100 priorto the components 205-220 being cut from the smart fabric 100. Therespective ends of the electrical conductors may be connected at theseam 230 by a person (e.g., a seamstress) while sewing the smart garment200 or by a robot configured to perform such operation. The respectiveends of the electrical conductors may be connected by soldering orwelding the respective ends of the electrical conductors together, orusing electrical connectors. The other components 205-220 of the smartgarment 200 can be sewn, and respective ends of electrical conductorscan be connected, in a similar manner.

At some point during manufacturing of the smart garment 200, for exampleafter the components 205-220 have been cut from the smart fabric 100,each of the components 205-220 can be scanned using an RF scanner, suchas an RFID scanner. The RFID scanner can be configured to scan eachcomponent 205-220 and communicate to the RFID tags of the respectivesensors 115 data indicating in which component 205-220, and where in thecomponent 205-220, the sensors 115 are integrated. For example, for alower part of the sleeve 210, a person or automated system can enterdata indicating “lower left sleeve” into the RFID scanner, and scan theportion of the smart fabric 100 in the lower part of the sleeve 210 withthe RFID scanner. The RFID tag in each sensor 115 of the lower part ofthe sleeve 210 can detect the RF signal emitted by the RFID scanner, andstore the data indicating “lower left sleeve” into the respectivecomputer readable storage medium. The process can be repeated for eachof the components 205-220, as well as different portions of thecomponents 205-220.

At some point after the electrical conductors have been connected, andperhaps after the smart garment 200 is sewn, a person or automatedsystem can provide to the processor 105 information identifying thesmart garment 200, such as a garment model number, serial number, size,color style, etc. For example, the person or automated system can scanthe processor with an RF device, such as an RFID scanner, which cancommunicate to the processor the data containing the identifyinginformation. The processor 105 can store the data in the computerreadable storage medium of the processor 105. In this regard, responsiveto the processor 105 receiving, via a receiver (e.g., an RF receiverthat is a component of a transceiver that includes the transmitter 110),an RF signal containing identifying information, the processor 105 canstore data.

Further, the person or automated system can initiate the processor 105to execute the program code of the processor 105 to retrieve baselinesensor data from the sensors 115 integrated into various the components205-220 of the smart garment 200 to generate baseline measurements forthe sensors 115. The processor can receive energy from the energy source120, or energy contained in a received RF signal, to generate thebaseline measurements, and can receive the baseline sensor data via theaforementioned electrical conductors. A person or automated system caninitiate the processor 105 to retrieve the baseline sensor data bydepressing a button integrated into the processor, or scanning theprocessor with an RF device. In the case an RF device is used,responsive to receiving an RF signal containing particular data, theprocessor 105 can execute computer program code that causes theprocessor to poll each of the sensors 115 integrated into the variousthe components 205-220 of the smart garment 200.

The processor 105 can store data received from each sensor 115 in one ormore data tables within the computer readable storage medium of theprocessor 105. The data retrieved from each sensor 115 can identify thespecific sensor 115, indicate in which component 205-220 respectivesensor 115 is integrated, and indicate a portion of the component205-220 in which the sensor 115 is integrated. The data also can includea baseline sensor reading, for example a tension reading, temperaturereading, moisture reading, etc. detected by the respective sensor 115.For each respective sensor 115, the processor 105 can create anassociation between the sensor identifier, the baseline sensor readingand the data indicating in which component 205-220, and in which portionof the components 205-220, the sensor is integrated. As each sensor 115is polled by the processor 105, the respective sensor 115 can use energycontained in the polling signal to perform the baseline sensor readingand communicate the various data to the processor 105. Once the baselinesensor measurements are stored by the processor 105, the smart garment200 is ready for packaging and sale. Of course, tags, etc. can be addedto the smart garment 200 if this is desired.

The processor 105 can be configured to monitor sensor data generated bythe sensors 115, and process the sensor data to determine if the smartgarment 200 is being worn by a user. For example, responsive to theprocessor detecting movement (e.g., using an accelerometer) or detectinga particular RF signal, the processor 105 can initiate execution ofprogram code to poll the sensors 115 to receive sensor data. When asensor 115 is proximate to a user's biological tissue (e.g., skin), thesensor 115 can measure a value of capacitance that is different from avalue of capacitance measured when the sensor 115 is not proximate tothe user's biological tissue (e.g., different from the baseline sensormeasurement). Thus, the processor 105 can be configured to determinethat the sensor 115 is proximate to biological tissue if the sensor 115generates a sensor value within a particular range of sensor values,which can be predetermined.

Responsive to the processor 105 receiving sensor data from a thresholdnumber of the sensors 115 indicating that each of those sensors 115 isproximate to biological tissue, the processor 105 can determine that thesmart garment 200 is being worn by a user. In response, the processor105 can monitor signals from other sensors 115 that indicate otherparameters, such as those previously described. The processor 105 canprocess such signals to determine the other parameters. Further, theprocessor 105 can store the parameters locally, within the processor 105and/or a computer readable storage medium (not shown) communicativelylinked to the processor 105, and/or communicate the parameters to one ormore other systems, as will be described.

FIG. 3 is a block diagram illustrating an example of a data processingenvironment (hereinafter “environment”) 300. The environment 300 caninclude the smart garment 200 of FIG. 2, and may include one or moreadditional smart garments. The environment 300 also can include one ormore servers 310, one or more client devices 320 and one or morereceivers (e.g., transceivers) 330. The client device(s) 320 and RFreceiver(s) 330 can be communicatively liked to the server(s) 310 via acommunication network 340.

The communication network 340 is the medium used to providecommunications links between various devices and systems connectedtogether within the environment 300. The communication network 340 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. The communication network 340 can be implemented as,or include, any of a variety of different communication technologiessuch as a WAN, a LAN, a wireless network, a mobile network, a VirtualPrivate Network (VPN), the Internet, the Public Switched TelephoneNetwork (PSTN), or similar technologies.

The server 310 can include a garment application 350 executable by oneor more processors of the server 310, and store user profiles 360 forvarious users, including a user 365 of the smart garment 200 (e.g., aperson who wears the smart garment 200). The server 310 can store theuser profiles 360 locally or on one or more computer-readable storagedevices communicatively linked to the server 310. The garmentapplication 350 can host a user interface in which users interact withthe server 310 via the client device(s) 320, or interface with one ormore mobile application via which the users interact with the server310.

Each RF receiver 330 can be configured to send and receive RF signalscommunicated in accordance with one more suitable RF communicationprotocols. For example, the RF receiver(s) 330 can communicate inaccordance with one or more of the IEEE 802-15 communication standardsand/or near field communication (NFC). The RF receiver(s) 330 canreceive garment data 370 from the transmitter 110 of the smart garment200, as well as receive garment data from transmitters from other smartgarments. The garment data 370 can include data generated by the sensors115. By way of example, at least one RF receiver 330 can be located in aresidence of the user 365, in a medical care facility (e.g., a hospital,a doctor's office, a diagnostic center, etc.). In one aspect, multiplereceivers 325 can be located in the user's place of residence and/ormultiple receivers 325 can be located in a medical care facility. Forinstance, an RF receiver 330 can be located in a room of user's place ofresidence, an RF receiver 330 can be located in a room or area of themedical care facility, and so on. The client device(s) 320 and RFreceiver(s) 330 also can be communicatively linked to the server(s) 310via the communication network 340.

The garment application 350 can receive sensor data generated by thesensors 115 of the smart garments 200 as garment data 370. The garmentapplication 350 can receive the garment data 370 via the RF receiver(s)330 and the communication network 340. In response to receiving thegarment data 370, the garment application 350 can process the garmentdata 370 and, based on such processing, make any of a myriad ofdeterminations regarding the user 365 and/or the smart garment 200.Further, the garment application 350 can communicate to the clientdevice(s) 320 information 380 indicating the garment data 370, resultsof the determinations regarding the user 365 based on the garment data370 and/or any other information.

In one arrangement, the garment application 350 can receive the garmentdata 370 each time a smart garment 200 is worn by a user 365. Thegarment data 370 for each instance of the smart garment 200 being worncan indicate a level of stretch in one or more portions of the smartgarment 200. The garment application 350 can compare the garment data370 from each instance of the smart garment 200 being worn by the user365 to the garment data 370 generated from previous instances of thesmart garment 200 being worn by the user 365. Based on such comparisonthe garment application 350 can determine at least one size changeparameter indicating at least one change in a size of the user 365.Based on the size change parameter, the garment application 350 candetermine whether a size of the smart garment 200 is not suitable forthe smart garment 200 to be worn by the user 365 (e.g., the smartgarment 200 is too small or too large for the user 365). Responsive todetermining that size of the smart garment 200 is not suitable for thegarment to be worn by the user 365, the garment application 350 canoutput an indication that the size of the smart garment is not suitablefor the smart garment to be worn by the user 365. For example, thegarment application 350 can output the indication in information 380 bycommunicating the information 380 to one or more client devices 320. Theclient device 320 can be a client device of the user 365 and/or a clientdevice of a care giver of the user 365.

In another arrangement, the garment data 370 generated by a particularsmart garment 200 can indicate whether a size of the smart garment 200is not suitable for the smart garment 200 to be worn by the user 365.For example, rather than the garment application 350 comparing datagenerated by the sensors 115 for various instances of the smart garment200 being worn, the processor 105 of the smart garment 200 can performsuch comparisons to determine at least one size change parameterindicating at least one change in a size of the user 365, and include inthe garment data 370 information indicating whether the size of thesmart garment 200 is suitable for the smart garment 200 to be worn bythe user 365. In such an arrangement, the processor 105 can communicatethe garment data 370 to the garment application 350 which, in turn, canoutput information 380 based on the garment data 370. In a furtherarrangement, the processor 105 can communicate the garment data 370directly to the client device(s) 320 via the RF receiver 330 and thecommunication network 340. In this regard, the processor 105 can outputan indication that the size of the smart garment 200 is not suitable forthe smart garment 200 to be worn by the user 365 by communicating theinformation 380 directly to the client device(s) 320.

The size of the smart garment 200 can be considered to be suitable to beworn by the user 365 if data indicating a stretch of the smart garment200 when worn by the user 365 is below a threshold value, or is betweena minimum threshold value and a maximum threshold value. Such data canbe garment data 370 generated by sensors 115 in any portion of the smartgarment 200, an average of garment data 370 generated by sensors 115 ina plurality of portions of the smart garment 200, and/or data generatedby processing garment data 370 generated by sensors 115 in a pluralityof portions of the smart garment 200.

In illustration, the garment application 350 (or the processor 105) canapply a weighting factor to the sensor data generated by the sensors 115based on the locations of the sensors 115. The garment application 350(or the processor 105) can specify the weighting factor. For example,the garment application 350 can specify a greater weight (e.g., aweighting value above a threshold value) to garment data 370 generatedby sensors in a first portion of the smart garment 200 (e.g., in aportion of the smart garment 200 that covers a user's abdomen) andspecify a lesser weight (e.g., a weighting value below a thresholdvalue) to garment data 370 generated by sensors in a second portion ofthe smart garment 200 (e.g., in a portion of the smart garment 200 thatcovers a user's bicep).

For example, the garment data 370 can include data generated by thesensors 115 indicating a level of stretch of the smart fabric from whichthe smart garment 200 is made. If the level of stretch is below a firstthreshold value, the garment application 350 can determine that the sizeof the smart garment 200 is too large for the user 365. If the level ofstretch is above a second threshold value, the garment application 350can determine that the size of the smart garment 200 is too small forthe user 365. If the level of stretch is between the first thresholdvalue and the second threshold value, the garment application 350 candetermine that the size of the smart garment 200 is suitable for theuser 365.

In one aspect of the present arrangements, the garment application 350(or processor 105) can, based on the garment data 370, determine agarment size that is suitable for the user 365. In illustration, thegarment data 370 can indicate a particular smart garment 200, a size ofthe smart garment 200, and a level of stretch in one or more portions ofthe smart garment 200 when the smart garment 200 is worn by the user365. The garment application 350 (or processor 105) can, based on thesize of the smart garment 200 and the level of stretch in one or moreportions of the smart garment 200 when the smart garment 200 is worn bythe user 365, determine a suitable size of the garment for the user. Thegarment application 350 (or processor 105) can indicate such suitablesize in the information 380 communicated to the client device(s) 320.Such indication can be provided responsive to the garment application350 (or processor 105) determining that size of the smart garment 200 isnot suitable for the garment to be worn by the user 365.

In a further aspect of the present arrangements, the garment application350 (or processor 105) can determine one or more other particulargarments that are different from the smart garment 200. For example, theother garments can be different styles and/or different types from thesmart garment 200, and may be different in size from the smart garment200. Responsive to determining that size of the smart garment 200 is notsuitable for the smart garment 200 to be worn by the user 365, thegarment application 350 (or processor 105) can output an indication of aparticular garment having a garment size that is suitable for the user365 in the information 380 communicated to the client device(s) 320.Such indication can be provided responsive to the garment application350 (or processor 105) determining that size of the smart garment 200 isnot suitable for the garment to be worn by the user 365.

In other aspects of the present arrangements, the garment data 370 caninclude biometric parameters indicating a health of the user 365.

In illustration, one or more portions of the sensors 115 can beconfigured to measure biometric parameters of the user 365. Thebiometric parameters can include, but are not limited to, a bodytemperature of the user 365, a heart rate of the user 365, a respirationrate of the user 365, a level of perspiration rate of the user 365, ablood pressure of the user 365, a blood sugar level of the user 365, andso on. In such an arrangement, the smart garment 200 can include sensors115 configured to detect body temperatures of the user 365, respirationrates of the user 365, perspiration rates (e.g., levels of bodymoisture) of the user 365, blood pressure of the user 365, blood sugarlevels of the user 365, etc. In some arrangements, the smart garment 200may include additional devices in addition to the sensors 115,transmitter 110 and processor 105. For example, the smart garment mayinclude an inflatable cuff controllable by the processor 105 to inflateand deflate for blood pressure measurements in accordance with knownblood pressure measurement techniques. In one aspect of the presentarrangements, the smart garment 200 can be a gown worn by the user 365while in a medical care facility.

The garment application 350 can analyze the biometric parameters and,based on such analysis, provide recommendations to the user 365 or acare giver of the user 365 in the information 380 communicated to theclient device 320. For example, based on analyzing the biometricparameters, the garment application 350 can determine patterns in thebiometric parameters (e.g., medical patterns). Based on the determinedpatterns, the garment application 350 can determine the recommendations.In illustration, the information 380 can include a recommendation forthe user to visit a health care facility and/or a medical practitioner,a recommendation for the user to rest, a recommendation for the user toperform exercises, a recommendation for the user to take medication, andso on. In this regard, the information 380 can include recommendationsfor the user to take at least one action to improve the user's health.

Further, the garment application 350 can analyze the biometricparameters, as well as other parameters (e.g., determined size changeparameters indicating weight gain or weight loss) and, based on suchanalysis, determine a risk of disease for the user 365. For example, thegarment application 350 can determine a risk of the user 365 having oneor more diseases. In another example, the garment application 350 candetermine a risk that user 365 may contract one or more diseases. Thegarment application 350 also can determine whether any such risks exceeda threshold value. If so, the garment application 350 can generate oneor more notifications regarding such risks, and communicate thenotifications to the client device(s) 320 in the information 380. Thenotifications can be presented to the user 365 or a care giver of theuser 365.

By way of example, as noted, based on the sensor data the garmentapplication 350 can determine size change parameters indicating a changein size of the user 365. The size change parameters can indicate aparticular portion of the user's body that has changed in size, forexample in the abdominal region. If the size of the abdominal region hasincreased, this can indicate a weight gain. The amount of tensionmeasured by sensors 115 that detect tension, along with the size of thesmart garment 200, can indicate a body mass index for the user 365,which the garment application 350 can determine based on processing thegarment data 370. Based on the body mass index of the user 365 andvarious biometric parameters generated by the sensors 115, for exampleblood pressure, heart rate and/or respiration rate, the garmentapplication 350 can determine a risk of the user 365 having a heartdisease.

To perform the described analyses of the various parameters, the garmentapplication 350 can interface with one or more medical databases, whichmay be components of the server(s) 310 or a components of externalresources (not shown), and access medical data that correlates symptomsto diseases, health recommendations, etc. The garment application 350can analyze the various parameters using the medical data to arrive atvarious medically related determinations described herein. Further, thegarment application 350 can perform predictive analysis, which will bedescribed herein, to predict symptoms indicated by various parameters.In illustration, an increase in the size of the user's abdominal regionmay be due to increased obesity, but may be due to other factors, forexample pregnancy. Using predictive analysis, the garment application350 can predict symptoms that cause the changes, and use suchpredictions when analyzing the various parameters to arrive at thevarious determinations.

Moreover, the garment application 350 can be configured to, based on thebiometric parameters indicated in the garment data 370, determine atleast one health change parameter indicating at least one change in thestate of health of the user 365. For example, the garment application350 can monitor, over time, various biometric parameters for the user365 indicated in the garment data 370. Based on such monitoring, thegarment application 350 can identify changes in the biometric parametersover time. Based on the at least one health change parameter indicatingthe at least one change in the state of health of the user 365, thegarment application 350 can determine whether the at least one change inthe state of health of the user exceeds a threshold value. If so, inresponse, the garment application 350 can output an indication that theat least one change in the state of health of the user 365 exceeds thethreshold value, for example in the information 380 communicated to theclient device(s) 320.

By way of example, at least a portion of the sensors 115 can beconfigured to detect a respiration rate and/or heart rate of the user365 and communicate corresponding biometric parameters to the processor105. The processor 105 can be configured to monitor and process suchbiometric parameters. Responsive to identifying, based on processing thebiometric parameters, a change in the user's respiration rate and/orheart rate that exceeds a threshold value, for example within apredetermined period of time, the processor 105 can output an indicationthat the at least one change in the state of health of the user exceedsthe threshold value. In one aspect, the processor 105 can output theindication in garment data 370 communicated to the garment application350. In response, the garment application 350 can communicateinformation 380 to one or more client device(s) 320 alerting persons,who may or may not include the user 365, of the indication that the atleast one change in the state of health of the user exceeds thethreshold value. In another aspect, the processor 105 can communicatethe information 380 directly to the client device(s) to alert persons,who may or may not include the user 365, of the indication that the atleast one change in the state of health of the user exceeds thethreshold value. It should be noted that the present arrangements arenot limited to respiration rate and heart rate, and the above processesalso can be applied to biometric parameters indicating the user'stemperature, blood pressure, or any other biometric parameters thesensors 115 may detect.

In one arrangement, outputting the indication that the at least onechange in the state of health of the user 365 exceeds the thresholdvalue can include generating an alert. In illustration, information 380communicated to the client device(s) 320 can include an alert. Moreover,an alert can be communicated to various other devices, for exampleoutput audio transducers (e.g., loudspeakers) configured to propagateaudio alert signals, indication lights configured to propagate visualalert signals, and so on. Further, the information 380 can includerecommendations for the user to take at least one action to improve theuser's health. For example, the information 380 can includerecommendations to take actions predicted to mitigate health risksresulting from the change in the state of health of the user 365. Inillustration, if the user's heart rate is above a threshold level, thegarment application 350 can generate a recommendation for the user torest, to take medication and/or to seek medical attention. Still, theinformation 380 can include any of a myriad of recommendations based onthe change in the state of health of the user 365, and the presentarrangements are not limited in this regard.

The determination of the information 380 also can be based on any of amyriad of data from the user profiles 360 and other data. For example,the information can be based on the user profiles 360, environmentaldata, fabric and clothing data, and so on. The user profiles 360 caninclude user data including, but not limited to, name, age bodymeasurements, biometric data, activity data (including physicalactivity), size and material preferences, clothing feedback, familyhealth history (including likelihood of developing diseases), healthhistory (including allergy history), etc. The environmental data caninclude weather data, allergen information (including allergy tracking,pollen count and pollen forecast), ambient temperature, etc. The fabricand clothing data can include material, dimensions, weight, thickness,color, density, state (dry, wet, stretching data etc.), clothing articleidentifier and/or serial number, and so on.

FIG. 4 is a block diagram illustrating example architecture for a server310. The server 310 can include at least one processor 405 (e.g., acentral processing unit) coupled to memory elements 410 through a systembus 415 or other suitable circuitry. As such, the server 310 can storeprogram code within the memory elements 410. The processor 405 canexecute the program code accessed from the memory elements 410 via thesystem bus 415. It should be appreciated that the server 310 can beimplemented in the form of any system including a processor and memorythat is capable of performing the functions and/or operations describedwithin this specification.

The memory elements 410 can include one or more physical memory devicessuch as, for example, local memory 420 and one or more bulk storagedevices 425. Local memory 420 refers to random access memory (RAM) orother non-persistent memory device(s) generally used during actualexecution of the program code. The bulk storage device(s) 425 can beimplemented as a hard disk drive (HDD), solid state drive (SSD), orother persistent data storage device. The server 310 also can includeone or more cache memories (not shown) that provide temporary storage ofat least some program code in order to reduce the number of timesprogram code must be retrieved from the bulk storage device 425 duringexecution.

One or more network adapters 430 can be coupled to server 310 via thesystem bus 415 to enable the server 310 to become coupled to othersystems, computer systems, remote printers, and/or remote storagedevices through intervening private or public networks. Modems, cablemodems, transceivers, and Ethernet cards are examples of different typesof network adapters 430 that can be used with the server 310.

As pictured in FIG. 4, the memory elements 410 can store the componentsof the server 310, namely an operating system 435, the garmentapplication 350 and the user profiles 360. Being implemented in the formof executable program code, the operating system 435 and the garmentapplication 350 can be executed by the server 310 and, as such, can beconsidered part of the server 310. Moreover, the operating system 435,the garment application 350 and the user profiles 360 are functionaldata structures that impart functionality when employed as part of theserver 310.

The garment application 350 can include various components, for example,a delta detector 440, a predictive analyzer 445, a prescriptive analyzer450 and a cognitive filter 455. The delta detector 440, predictiveanalyzer 445, prescriptive analyzer 450 and a cognitive filter 455 alsoare functional data structures that impart functionality when employedas part of the server 310.

The delta detector 440 can, based various parameters generated by thesensors 115 of FIG. 3, detect and record changes in the states of smartgarments 200 and/or biometric states of the user. Examples of thechanges in state include, but are not limited to, stretching of thesmart garment 200 when worn, body temperature changes of the user, heartrate changes of the user, perspiration rate changes of the user, bloodpressure changes of the user, blood sugar level changes of the user, andso on. The delta detector 440 also can measure levels of changes in thestates, and record such levels as delta values. Moreover, the deltadetector 400 can record with the detected changes and the determineddelta values an indication parameters indicating where on the smartgarment 200 the sensors 115 are located that generated the sensor dataused to determine the detected changes and the determined delta values.Such parameters can facilitate analysis of the sensor data. For example,a person who is exercising and sweating may sweat more profusely incertain areas, and those areas may be known to have a higher temperatureand moisture than other areas during exercise. Accordingly, whenanalyzing the data, the garment application 350 can predict that theincreased moisture and temperature in those areas are a result ofexercising instead of a being due to a medical issue. In anotherexample, if a person is walking in the rain, it is more likely that theywill have moisture on their shoulders, chest area and back than otherareas of the body. If, when analyzing the data, the garment application350 detects a higher level of moisture in those areas, and the garmentapplication 350 accesses data indicating rainy weather conditions, thegarment application 350 can predict that the moisture is due to rainrather than being due to a medical issue.

As noted, the garment application 350 can detect different levels oftension (e.g., stretch) in different areas of the smart garment 200.Further, the delta detector 440 can detect a difference in tension, orstretch, between when the garment is worn and the garment is not worn,and record corresponding delta values for various regions of the smartgarment 200. The delta detector 400 also can determine additional deltavalues representing the change in the tension over time, for example asthe user gains or loses weight. The garment application 350 can analyzethe various delta values to detect and learn about changes in the user'sbody over time. Such changes can be considered by the garmentapplication 350 when performing various analyses, such as thosepreviously described with respect to FIG. 3.

The predictive analyzer 445 can process sensor data received from thesensors 115 in the garment data 370 to help determine the previouslydescribed risk of disease. For example, the waist size of the user canbe a parameter that is analyzed to predict accurately the user's risk ofexperiencing heart disease. As noted, however, a person's waist size canvary for various reasons, and the predictive analyzer 445 can processwaist size parameters along with various other detected and/or generatedparameters to predict a cause of the change in waist size. For example,delta values corresponding to tension around the waste can indicate inincrease in waist size corresponding to pregnancy, rather than obesity.Using predictive analytics, the predictive analyzer 445 can identifysuch circumstance. The garment application 350 can use the results ofthe prediction when determine various medical analyses for the user,such as those previously described. Moreover, as the delta detector 440generated delta values over time, the predictive analyzer 445 can updatevarious predictions for the user.

The prescriptive analyzer 450 can determine various recommendations forthe user based on the sensor data generated by the sensors 115, datagenerated by the delta detector 440, and data generated by thepredictive analyzer 445. As noted, examples of such recommendations caninclude a recommendation to see a medical professional, a recommendationto increase or decrease levels of physical activity, etc. Other examplesinclude a recommendation to increase or decrease caloric, nutritional orsupplemental indicate, a recommendation to purchase clothing in adifferent size, recommendation to purchase clothing using a differentfabric or material, etc. Because the user's data may change of time, theprescriptive analyzer 450 can update recommendations, but also mayre-prescribe other recommendations that still are relevant to the user.

The cognitive filter 455 can implement cognitive analysis, which isknown in the art, to track garment data 370 from various smart garments200 worn or purchased by the user, and learn the user's clothingpreferences. Further, the cognitive filter 455 can learn which sizes ofclothing fit the user for various brands. For example, the user may wearsize 4 in clothing from a first manufacturer, but wear size 6 inclothing from a second manufacturer, and the cognitive filter 455 canlearn and store corresponding data. Still, the cognitive filter 455 canperform various other types of cognitive analysis, and the presentarrangements are not limited in this regard.

FIG. 5 is a flow chart illustrating an example of a method 500 ofoutputting a notification regarding a size of a smart garment. Themethod 500 can be implemented by the server 310 (e.g., the garmentapplication 350) or the smart garment 200 (e.g., the processor 105) ofFIG. 1. The following description discusses implementation of the method500 by the garment application 350, but it will be understood that theprocessor 105 can implement the method 500 using suitable configuredcomputer program code.

At step 502, the garment application 350 can receive sensor datagenerated by at least a first portion of a plurality of sensorsintegrated into a smart garment, the sensor data indicating a level atwhich fabric of the smart garment is stretched when worn by a user. Atstep 504, the garment application 350 can determine, using a processor,based on the sensor data, at least one size change parameter indicatingat least one change in a size of the user. At step 506, the garmentapplication 350 can, based on the at least one size change parameterindicating the at least one change in the size of the user, determinewhether a size of the smart garment is not suitable for the smartgarment to be worn by the user. At step 508, the garment application 350can, responsive to determining that size of the smart garment is notsuitable for the smart garment to be worn by the user, output anotification indicating that the size of the smart garment is notsuitable for the smart garment to be worn by the user. At step 510, thegarment application 350 can, responsive to determining that size of thesmart garment is not suitable for the garment to be worn by the user,output another notification indicating a garment size that is suitablefor the user. At step 512, the garment application 350 can, responsiveto determining that size of the smart garment is not suitable for thegarment to be worn by the user, output another notification indicating aparticular garment having a garment size that is suitable for the user.

FIG. 6 is a flow chart illustrating an example of a method 600 ofoutputting a notification regarding a risk of disease for a user. Themethod 600 can be implemented by the server 310 (e.g., the garmentapplication 350) of FIG. 1.

At step 602, the garment application 350 can receive sensor datagenerated by at least a portion of a plurality of sensors integratedinto a smart garment, the sensor data indicating at least one biometricparameter indicating a state of health of a user. At step 604, thegarment application 350 can determine, based on the sensor data, a riskof disease for the user. At step 606, the garment application 350 candetermine whether the risk of the disease for the user exceeds athreshold value. At step 608, the garment application 350 can,responsive to determining that the risk of the disease for the userexceeds the threshold value, output a notification indicating the riskof the disease for the user.

FIG. 7 is a flow chart illustrating an example of a method 700 ofoutputting a notification regarding a change in the state of health of auser. The method 700 can be implemented by the server 310 (e.g., thegarment application 350) of FIG. 1.

At step 702, the garment application 350 can receive sensor datagenerated by at least a portion of the plurality of sensors integratedinto the smart garment, the sensor data indicating at least onebiometric parameter indicating a state of health of a user. At step 704,the garment application 350 can determine, based on the sensor data, atleast one health change parameter indicating at least one change in thestate of health of the user. At step 706, the garment application 350can, based on the at least one health change parameter indicating the atleast one change in the state of health of the user, determine whetherthe at least one change in the state of health of the user exceeds athreshold value. At step 708, the garment application 350 can,responsive to determining that the at least one change in the state ofhealth of the user exceeds the threshold value, output a notificationindicating that the at least one change in the state of health of theuser exceeds the threshold value. At step 710, the garment application350 can output another notification indicating a recommendation for theuser to take at least one action to mitigate a health risk resultingfrom the change in the state of health of the user.

While the disclosure concludes with claims defining novel features, itis believed that the various features described herein will be betterunderstood from a consideration of the description in conjunction withthe drawings. The process(es), machine(s), manufacture(s) and anyvariations thereof described within this disclosure are provided forpurposes of illustration. Any specific structural and functional detailsdescribed are not to be interpreted as limiting, but merely as a basisfor the claims and as a representative basis for teaching one skilled inthe art to variously employ the features described in virtually anyappropriately detailed structure. Further, the terms and phrases usedwithin this disclosure are not intended to be limiting, but rather toprovide an understandable description of the features described.

For purposes of simplicity and clarity of illustration, elements shownin the figures have not necessarily been drawn to scale. For example,the dimensions of some of the elements may be exaggerated relative toother elements for clarity. Further, where considered appropriate,reference numbers are repeated among the figures to indicatecorresponding, analogous, or like features.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “includes,”“including,” “comprises,” and/or “comprising,” when used in thisdisclosure, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

Reference throughout this disclosure to “one embodiment,” “anembodiment,” or similar language means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment described within this disclosure.Thus, appearances of the phrases “in one embodiment,” “in anembodiment,” and similar language throughout this disclosure may, but donot necessarily, all refer to the same embodiment.

The term “plurality,” as used herein, is defined as two or more thantwo. The term “another,” as used herein, is defined as at least a secondor more. The term “coupled,” as used herein, is defined as connected,whether directly without any intervening elements or indirectly with oneor more intervening elements, unless otherwise indicated. Two elementsalso can be coupled mechanically, electrically, or communicativelylinked through a communication channel, pathway, network, or system. Theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill also be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms, as these terms are only used to distinguishone element from another unless stated otherwise or the contextindicates otherwise.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1-20. (canceled)
 21. A method, comprising: receiving first sensor datagenerated by at least a first portion of a plurality of sensorsintegrated into a smart garment, the first sensor data indicating atleast one biometric parameter indicating a state of health of a userwearing the smart garment; determining, using a processor, based on thefirst sensor data, at least one health change parameter indicating atleast one change in the state of health of the user; based on the atleast one health change parameter indicating the at least one change inthe state of health of the user, determining whether the at least onechange in the state of health of the user exceeds a threshold value; andresponsive to determining that the at least one change in the state ofhealth of the user exceeds the threshold value, outputting a firstnotification indicating that the at least one change in the state ofhealth of the user exceeds the threshold value.
 22. The method of claim21, further comprising: outputting a second notification indicating arecommendation for the user to take at least one action to mitigate ahealth risk resulting from the change in the state of health of theuser.
 23. The method of claim 22, further comprising: analyzing the atleast one biometric parameter; and based on the analyzing the at leastone biometric parameter, determining at least one pattern in the atleast one biometric parameter; wherein outputting the secondnotification indicating the recommendation for the user to take the atleast one action to mitigate the health risk resulting from the changein the state of health of the user comprises determining therecommendation based on the determined at least one pattern in the atleast one biometric parameter.
 24. The method of claim 21, furthercomprising: determining, based on the first sensor data, a risk ofdisease for the user; determining whether the risk of the disease forthe user exceeds a threshold value; and responsive to determining thatthe risk of the disease for the user exceeds the threshold value,outputting a second notification indicating the risk of the disease forthe user.
 25. The method of claim 24, further comprising: analyzing theat least one biometric parameter; and based on the analyzing the atleast one biometric parameter, determining at least one pattern in theat least one biometric parameter; wherein outputting the secondnotification indicating the risk of the disease for the user comprisesdetermining the risk of the disease for the user based on the determinedat least one pattern in the at least one biometric parameter.
 26. Themethod of claim 25, further comprising: receiving second sensor datagenerated by at least a second portion of the plurality of sensorsintegrated into the smart garment, the second sensor data indicating atleast one size change parameter that indicates a weight gain of the useror a weight loss of the user; wherein the determining the risk of thedisease for the user further is based on the second sensor dataindicating the at least one size change parameter.
 27. The method ofclaim 21, wherein the biometric parameter is a parameter selected from agroup consisting of a body temperature parameter, a heart rateparameter, and a blood pressure parameter.
 28. The method of claim 21,wherein the biometric parameter is a parameter selected from a groupconsisting of a level of perspiration rate parameter and a blood sugarlevel parameter.
 29. A system, comprising: a processor programmed toinitiate executable operations comprising: receiving first sensor datagenerated by at least a first portion of a plurality of sensorsintegrated into a smart garment, the first sensor data indicating atleast one biometric parameter indicating a state of health of a userwearing the smart garment; determining, based on the first sensor data,at least one health change parameter indicating at least one change inthe state of health of the user; based on the at least one health changeparameter indicating the at least one change in the state of health ofthe user, determining whether the at least one change in the state ofhealth of the user exceeds a threshold value; and responsive todetermining that the at least one change in the state of health of theuser exceeds the threshold value, outputting a first notificationindicating that the at least one change in the state of health of theuser exceeds the threshold value.
 30. The system of claim 29, theexecutable operations further comprising: outputting a secondnotification indicating a recommendation for the user to take at leastone action to mitigate a health risk resulting from the change in thestate of health of the user.
 31. The system of claim 30, the executableoperations further comprising: analyzing the at least one biometricparameter; and based on the analyzing the at least one biometricparameter, determining at least one pattern in the at least onebiometric parameter; wherein outputting the second notificationindicating the recommendation for the user to take the at least oneaction to mitigate the health risk resulting from the change in thestate of health of the user comprises determining the recommendationbased on the determined at least one pattern in the at least onebiometric parameter.
 32. The system of claim 29, the executableoperations further comprising: determining, based on the first sensordata, a risk of disease for the user; determining whether the risk ofthe disease for the user exceeds a threshold value; and responsive todetermining that the risk of the disease for the user exceeds thethreshold value, outputting a second notification indicating the risk ofthe disease for the user.
 33. The system of claim 32, the executableoperations further comprising: analyzing the at least one biometricparameter; and based on the analyzing the at least one biometricparameter, determining at least one pattern in the at least onebiometric parameter; wherein outputting the second notificationindicating the risk of the disease for the user comprises determiningthe risk of the disease for the user based on the determined at leastone pattern in the at least one biometric parameter.
 34. The system ofclaim 33, the executable operations further comprising: receiving secondsensor data generated by at least a second portion of the plurality ofsensors integrated into the smart garment, the second sensor dataindicating at least one size change parameter that indicates a weightgain of the user or a weight loss of the user; wherein the determiningthe risk of the disease for the user further is based on the secondsensor data indicating the at least one size change parameter.
 35. Thesystem of claim 29, wherein the biometric parameter is a parameterselected from a group consisting of a body temperature parameter, aheart rate parameter, and a blood pressure parameter.
 36. The system ofclaim 29, wherein the biometric parameter is a parameter selected from agroup consisting of a level of perspiration rate parameter and a bloodsugar level parameter.
 37. A computer program product comprising acomputer readable storage medium having program code stored thereon, theprogram code executable by a processor to perform a method comprising:receiving first sensor data generated by at least a first portion of aplurality of sensors integrated into a smart garment, the first sensordata indicating at least one biometric parameter indicating a state ofhealth a user wearing the smart garment; determining, by the processor,based on the first sensor data, at least one health change parameterindicating at least one change in the state of health of the user; basedon the at least one health change parameter indicating the at least onechange in the state of health of the user, determining whether the atleast one change in the state of health of the user exceeds a thresholdvalue; and responsive to determining that the at least one change in thestate of health of the user exceeds the threshold value, outputting afirst notification indicating that the at least one change in the stateof health of the user exceeds the threshold value.
 38. The computerprogram product of claim 37, the method further comprising: outputting asecond notification indicating a recommendation for the user to take atleast one action to mitigate a health risk resulting from the change inthe state of health of the user.
 39. The computer program product ofclaim 38, the method further comprising: analyzing the at least onebiometric parameter; and based on the analyzing the at least onebiometric parameter, determining at least one pattern in the at leastone biometric parameter; wherein outputting the second notificationindicating the recommendation for the user to take the at least oneaction to mitigate the health risk resulting from the change in thestate of health of the user comprises determining the recommendationbased on the determined at least one pattern in the at least onebiometric parameter.
 40. The computer program product of claim 37, themethod further comprising: determining, based on the first sensor data,a risk of disease for the user; determining whether the risk of thedisease for the user exceeds a threshold value; and responsive todetermining that the risk of the disease for the user exceeds thethreshold value, outputting a second notification indicating the risk ofthe disease for the user.